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RI Seminar -- Patrick J. Flynn

Assistant Professor
School of Electrical Engineering and Computer Science,
Washington State University

TITLE: Integration of Multiple Feature Groups and Multiple Views Into
A 3D Object Recognition System

ABSTRACT

We propose two approaches for utilizing multiple-feature group (triples) and
multiple-view information to reduce the number of hypotheses passed to the
verification stage in an invariant feature indexing (IFI)-based object
recognition system. The first approach is based on a majority voting scheme
that keeps track of the number of consistent votes cast by prototype
hypotheses for particular object models. The second approach examines
the consistency of estimated object pose from multiple scene-triples of a
single view or multiple views. Monte Carlo experiments employing
500 single-view synthetic range images and 195 pairs of synthetic
range images with a large CAD-based 3D object
database show that a significant number of hypotheses can be
eliminated by using these approaches. The proposed approaches have also been
tested on real range images of several objects. A salient feature of our
system and experiment design compared to most existing 3D object recognition
systems is our use of a large object data base and a large number of test
images.